1. Field of the Invention
The present invention relates to computerized statistical or combinatorial procedures and methods, and particularly to a facilities optimization method implemented on a computer for selecting efficient facility locations based on supply and demand laws.
2. Description of the Related Art
Facility layout and planning is an important topic that has a wide variety of applications in real life. Both private and public sectors are often faced with problems involving facility layout decisions. Facility location, for example, is concerned with the finding the best locations for facilities based on supply-demand requirements. This problem has many applications in real life including locating retail stores, schools, hospitals, ambulance bases, fire stations, automatic teller machines, gas stations and wireless base stations.
Design parameters of the facility location problem include how many facilities should be sited, where should each facility be located, how large each facility should be, and how should demand be allocated.
Modeling of the facility location problem has been investigated widely in the literature. Persons having ordinary skill in the art have categorized the problem into different types of models including set covering, maximum covering, P-center and P-Median models. Models can also be planar, network, or discrete. Static as well as dynamic models are also considered in the literature. In static models the inputs to the problem do not change with time while in dynamic models, the inputs are dependent on time.
Other categories of the location problem involve elastic versus inelastic demand, capacitated versus uncapacitated facility, deterministic versus probabilistic models. Different distance metrics are considered in these models, including the Manhattan (right-angle), Euclidean (straight-line), and lp metrics.
Solution of the facility location problem has also been discussed extensively in the literature. Linear and integer programming are used widely to solve location problems. Other common approaches well known to practitioners of ordinary skill are used, including tabu search, simulated annealing and genetic algorithm. These approaches show a considerable amount of success in solving particular location problems. However, every one of these approaches has its own limitation. Some of these approaches are difficult to understand and implement, requiring an expert's input. In addition, the formulation of the problem in most of these methods is not straight-forward. Furthermore, they all tend to have high computation cost especially for problems with large dimension. Finally, a solution is not always guaranteed and may be sensitive to the modeling parameters.
Thus, a facilities optimization method solving the aforementioned problems is desired.